Download or read book SAS Survival Analysis Techniques for Medical Research written by Alan B. Cantor and published by SAS Press. This book was released on 2003 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you are new to survival analysis or want to expand your capabilities in this area, you'll benefit from Alan Cantor's SAS Survival Analysis Techniques for Medical Research, Second Edition, which presents the theory and methods of survival analysis along with excellent discussions of the SAS procedures used to implement the methods described. New features of the second edition include a discussion of permutation and randomization tests; a discussion of the use of data imputation; an expanded discussion of power for Cox regression; descriptions of the new features of SAS 9, such as confidence bands for the Kaplan-Meier curve; appendixes that cover mathematical and statistical background topics needed in survival analysis; and student exercises. The new features, along with several useful macros and numerous examples, make this a suitable textbook for a course in survival analysis for biostatistics majors and majors in related fields. This book excels at presenting complex ideas in a way that enables those without a strong technical background to understand and apply the concepts and techniques.
Download or read book Extending SAS Survival Analysis Techniques for Medical Research written by Alan B. Cantor and published by . This book was released on 1997 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Survival Analysis Using SAS written by Paul D. Allison and published by SAS Institute. This book was released on 2010-03-29 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: Easy to read and comprehensive, Survival Analysis Using SAS: A Practical Guide, Second Edition, by Paul D. Allison, is an accessible, data-based introduction to methods of survival analysis. Researchers who want to analyze survival data with SAS will find just what they need with this fully updated new edition that incorporates the many enhancements in SAS procedures for survival analysis in SAS 9. Although the book assumes only a minimal knowledge of SAS, more experienced users will learn new techniques of data input and manipulation. Numerous examples of SAS code and output make this an eminently practical book, ensuring that even the uninitiated become sophisticated users of survival analysis. The main topics presented include censoring, survival curves, Kaplan-Meier estimation, accelerated failure time models, Cox regression models, and discrete-time analysis. Also included are topics not usually covered in survival analysis books, such as time-dependent covariates, competing risks, and repeated events. Survival Analysis Using SAS: A Practical Guide, Second Edition, has been thoroughly updated for SAS 9, and all figures are presented using ODS Graphics. This new edition also documents major enhancements to the STRATA statement in the LIFETEST procedure; includes a section on the PROBPLOT command, which offers graphical methods to evaluate the fit of each parametric regression model; introduces the new BAYES statement for both parametric and Cox models, which allows the user to do a Bayesian analysis using MCMC methods; demonstrates the use of the counting process syntax as an alternative method for handling time-dependent covariates; contains a section on cumulative incidence functions; and describes the use of the new GLIMMIX procedure to estimate random-effects models for discrete-time data. This book is part of the SAS Press program.
Download or read book Survival Analysis written by John P. Klein and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: Making complex methods more accessible to applied researchers without an advanced mathematical background, the authors present the essence of new techniques available, as well as classical techniques, and apply them to data. Practical suggestions for implementing the various methods are set off in a series of practical notes at the end of each section, while technical details of the derivation of the techniques are sketched in the technical notes. This book will thus be useful for investigators who need to analyse censored or truncated life time data, and as a textbook for a graduate course in survival analysis, the only prerequisite being a standard course in statistical methodology.
Download or read book Modelling Survival Data in Medical Research written by D. Collett and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Fourth edition has new chapters on Bayesian survival analysis and use of the R software. Chapters extensively revised, expanded to add new material on topics that include methods for assessing predictive ability of a model, joint models for longitudinal and survival data, modern methods for the analysis of interval-censored survival data"--
Download or read book Applied Medical Statistics Using SAS written by Geoff Der and published by CRC Press. This book was released on 2012-10-01 with total page 539 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written with medical statisticians and medical researchers in mind, this intermediate-level reference explores the use of SAS for analyzing medical data. Applied Medical Statistics Using SAS covers the whole range of modern statistical methods used in the analysis of medical data, including regression, analysis of variance and covariance, longitudi
Download or read book Analysis of Clinical Trials Using SAS written by Alex Dmitrienko and published by SAS Institute. This book was released on 2017-07-17 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analysis of Clinical Trials Using SAS®: A Practical Guide, Second Edition bridges the gap between modern statistical methodology and real-world clinical trial applications. Tutorial material and step-by-step instructions illustrated with examples from actual trials serve to define relevant statistical approaches, describe their clinical trial applications, and implement the approaches rapidly and efficiently using the power of SAS. Topics reflect the International Conference on Harmonization (ICH) guidelines for the pharmaceutical industry and address important statistical problems encountered in clinical trials. Commonly used methods are covered, including dose-escalation and dose-finding methods that are applied in Phase I and Phase II clinical trials, as well as important trial designs and analysis strategies that are employed in Phase II and Phase III clinical trials, such as multiplicity adjustment, data monitoring, and methods for handling incomplete data. This book also features recommendations from clinical trial experts and a discussion of relevant regulatory guidelines. This new edition includes more examples and case studies, new approaches for addressing statistical problems, and the following new technological updates: SAS procedures used in group sequential trials (PROC SEQDESIGN and PROC SEQTEST) SAS procedures used in repeated measures analysis (PROC GLIMMIX and PROC GEE) macros for implementing a broad range of randomization-based methods in clinical trials, performing complex multiplicity adjustments, and investigating the design and analysis of early phase trials (Phase I dose-escalation trials and Phase II dose-finding trials) Clinical statisticians, research scientists, and graduate students in biostatistics will greatly benefit from the decades of clinical research experience and the ready-to-use SAS macros compiled in this book.
Download or read book Survival Analysis written by David G. Kleinbaum and published by Springer Science & Business Media. This book was released on 2013-04-18 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: A straightforward and easy-to-follow introduction to the main concepts and techniques of the subject. It is based on numerous courses given by the author to students and researchers in the health sciences and is written with such readers in mind. A "user-friendly" layout includes numerous illustrations and exercises and the book is written in such a way so as to enable readers learn directly without the assistance of a classroom instructor. Throughout, there is an emphasis on presenting each new topic backed by real examples of a survival analysis investigation, followed up with thorough analyses of real data sets. Each chapter concludes with practice exercises to help readers reinforce their understanding of the concepts covered, before going on to a more comprehensive test. Answers to both are included. Readers will enjoy David Kleinbaums style of presentation, making this an excellent introduction for all those coming to the subject for the first time.
Download or read book Business Survival Analysis Using SAS written by Jorge Ribeiro and published by Independently Published. This book was released on 2022-01-27 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solve business problems involving time-to-event and resulting probabilities by following the modeling tutorials in Business Survival Analysis Using SAS: An Introduction to Lifetime Probabilities, the first book to be published in the field of business survival analysis! Survival analysis is a challenge. Books applying to health sciences exist, but nothing about survival applications for business has been available until now. Written for analysts, forecasters, econometricians, and modelers who work in marketing or credit risk and have little SAS modeling experience, Business Survival Analysis Using SAS builds on a foundation of SAS code that works in any survival model and features numerous annotated graphs, coefficients, and statistics linked to real business situations and data sets. This guide also helps recent graduates who know the statistics but do not necessarily know how to apply them get up and running in their jobs. By example, it teaches the techniques while avoiding advanced theoretical underpinnings so that busy professionals can rapidly deliver a survival model to meet common business needs. From first principles, this book teaches survival analysis by highlighting its relevance to business cases. A pragmatic introduction to survival analysis models, it leads you through business examples that contextualize and motivate the statistical methods and SAS coding. Specifically, it illustrates how to build a time-to-next-purchase survival model in SAS Enterprise Miner, and it relates each step to the underlying statistics and to Base SAS and SAS/STAT software. Following the many examples-from data preparation to validation to scoring new customers-you will learn to develop and apply survival analysis techniques to scenarios faced by companies in the financial services, insurance, telecommunication, and marketing industries, including the following scenarios: Time-to-next-purchase for marketing Employer turnover for human resources Small business portfolio macroeconometric stress tests for banks International Financial Reporting Standard (IFRS 9) lifetime probability of default for banks and building societies "Churn," or attrition, models for the telecommunications and insurance industries
Download or read book Applied Survival Analysis Using R written by Dirk F. Moore and published by Springer. This book was released on 2016-05-11 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied Survival Analysis Using R covers the main principles of survival analysis, gives examples of how it is applied, and teaches how to put those principles to use to analyze data using R as a vehicle. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. Many survival methods are extensions of techniques used in linear regression and categorical data, while other aspects of this field are unique to survival data. This text employs numerous actual examples to illustrate survival curve estimation, comparison of survivals of different groups, proper accounting for censoring and truncation, model variable selection, and residual analysis. Because explaining survival analysis requires more advanced mathematics than many other statistical topics, this book is organized with basic concepts and most frequently used procedures covered in earlier chapters, with more advanced topics near the end and in the appendices. A background in basic linear regression and categorical data analysis, as well as a basic knowledge of calculus and the R system, will help the reader to fully appreciate the information presented. Examples are simple and straightforward while still illustrating key points, shedding light on the application of survival analysis in a way that is useful for graduate students, researchers, and practitioners in biostatistics.
Download or read book Pharmaceutical Statistics Using SAS written by Alex Dmitrienko, Ph.D. and published by SAS Institute. This book was released on 2007-02-07 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces a range of data analysis problems encountered in drug development and illustrates them using case studies from actual pre-clinical experiments and clinical studies. Includes a discussion of methodological issues, practical advice from subject matter experts, and review of relevant regulatory guidelines.
Download or read book Survival Analysis Using S written by Mara Tableman and published by CRC Press. This book was released on 2003-07-28 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: Survival Analysis Using S: Analysis of Time-to-Event Data is designed as a text for a one-semester or one-quarter course in survival analysis for upper-level or graduate students in statistics, biostatistics, and epidemiology. Prerequisites are a standard pre-calculus first course in probability and statistics, and a course in applied linear regression models. No prior knowledge of S or R is assumed. A wide choice of exercises is included, some intended for more advanced students with a first course in mathematical statistics. The authors emphasize parametric log-linear models, while also detailing nonparametric procedures along with model building and data diagnostics. Medical and public health researchers will find the discussion of cut point analysis with bootstrap validation, competing risks and the cumulative incidence estimator, and the analysis of left-truncated and right-censored data invaluable. The bootstrap procedure checks robustness of cut point analysis and determines cut point(s). In a chapter written by Stephen Portnoy, censored regression quantiles - a new nonparametric regression methodology (2003) - is developed to identify important forms of population heterogeneity and to detect departures from traditional Cox models. By generalizing the Kaplan-Meier estimator to regression models for conditional quantiles, this methods provides a valuable complement to traditional Cox proportional hazards approaches.
Download or read book Survival Analysis written by Xian Liu and published by John Wiley & Sons. This book was released on 2012-06-13 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: Survival analysis concerns sequential occurrences of events governed by probabilistic laws. Recent decades have witnessed many applications of survival analysis in various disciplines. This book introduces both classic survival models and theories along with newly developed techniques. Readers will learn how to perform analysis of survival data by following numerous empirical illustrations in SAS. Survival Analysis: Models and Applications: Presents basic techniques before leading onto some of the most advanced topics in survival analysis. Assumes only a minimal knowledge of SAS whilst enabling more experienced users to learn new techniques of data input and manipulation. Provides numerous examples of SAS code to illustrate each of the methods, along with step-by-step instructions to perform each technique. Highlights the strengths and limitations of each technique covered. Covering a wide scope of survival techniques and methods, from the introductory to the advanced, this book can be used as a useful reference book for planners, researchers, and professors who are working in settings involving various lifetime events. Scientists interested in survival analysis should find it a useful guidebook for the incorporation of survival data and methods into their projects.
Download or read book Applied Survival Analysis written by David W. Hosmer, Jr. and published by John Wiley & Sons. This book was released on 2011-09-23 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: THE MOST PRACTICAL, UP-TO-DATE GUIDE TO MODELLING AND ANALYZING TIME-TO-EVENT DATA—NOW IN A VALUABLE NEW EDITION Since publication of the first edition nearly a decade ago, analyses using time-to-event methods have increase considerably in all areas of scientific inquiry mainly as a result of model-building methods available in modern statistical software packages. However, there has been minimal coverage in the available literature to9 guide researchers, practitioners, and students who wish to apply these methods to health-related areas of study. Applied Survival Analysis, Second Edition provides a comprehensive and up-to-date introduction to regression modeling for time-to-event data in medical, epidemiological, biostatistical, and other health-related research. This book places a unique emphasis on the practical and contemporary applications of regression modeling rather than the mathematical theory. It offers a clear and accessible presentation of modern modeling techniques supplemented with real-world examples and case studies. Key topics covered include: variable selection, identification of the scale of continuous covariates, the role of interactions in the model, assessment of fit and model assumptions, regression diagnostics, recurrent event models, frailty models, additive models, competing risk models, and missing data. Features of the Second Edition include: Expanded coverage of interactions and the covariate-adjusted survival functions The use of the Worchester Heart Attack Study as the main modeling data set for illustrating discussed concepts and techniques New discussion of variable selection with multivariable fractional polynomials Further exploration of time-varying covariates, complex with examples Additional treatment of the exponential, Weibull, and log-logistic parametric regression models Increased emphasis on interpreting and using results as well as utilizing multiple imputation methods to analyze data with missing values New examples and exercises at the end of each chapter Analyses throughout the text are performed using Stata® Version 9, and an accompanying FTP site contains the data sets used in the book. Applied Survival Analysis, Second Edition is an ideal book for graduate-level courses in biostatistics, statistics, and epidemiologic methods. It also serves as a valuable reference for practitioners and researchers in any health-related field or for professionals in insurance and government.
Download or read book Modeling Survival Data Extending the Cox Model written by Terry M. Therneau and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyze multiple/correlated event data using marginal and random effects. The focus is on actual data examples, the analysis and interpretation of results, and computation. The book shows how these new methods can be implemented in SAS and S-Plus, including computer code, worked examples, and data sets.
Download or read book Statistical Analysis of Medical Data Using SAS written by Geoff Der and published by CRC Press. This book was released on 2005-09-20 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical analysis is ubiquitous in modern medical research. Logistic regression, generalized linear models, random effects models, and Cox's regression all have become commonplace in the medical literature. But while statistical software such as SAS make routine application of these techniques possible, users who are not primarily statisticians must take care to correctly implement the various procedures and correctly interpret the output. Statistical Analysis of Medical Data Using SAS demonstrates how to use SAS to analyze medical data. Each chapter addresses a particular analysis method. The authors briefly describe each procedure, but focus on its SAS implementation and properly interpreting the output. The carefully designed presentation relegates the theoretical details to "Displays," so that the code and results can be explored without interruption. All of the code and data sets used in the book are available for download from either the SAS Web site or www.crcpress.com. Der and Everitt, authors of the best-selling Handbook of Statistical Analyses Using SAS, bring all of their considerable talent and experience to bear in this book. Step-by-step instructions, lucid explanations and clear examples combine to form an outstanding, self-contained guide--suitable for medical researchers and statisticians alike--to using SAS to analyze medical data.
Download or read book Complex Survey Data Analysis with SAS written by Taylor H. Lewis and published by CRC Press. This book was released on 2016-09-15 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex Survey Data Analysis with SAS® is an invaluable resource for applied researchers analyzing data generated from a sample design involving any combination of stratification, clustering, unequal weights, or finite population correction factors. After clearly explaining how the presence of these features can invalidate the assumptions underlying most traditional statistical techniques, this book equips readers with the knowledge to confidently account for them during the estimation and inference process by employing the SURVEY family of SAS/STAT® procedures. The book offers comprehensive coverage of the most essential topics, including: Drawing random samples Descriptive statistics for continuous and categorical variables Fitting and interpreting linear and logistic regression models Survival analysis Domain estimation Replication variance estimation methods Weight adjustment and imputation methods for handling missing data The easy-to-follow examples are drawn from real-world survey data sets spanning multiple disciplines, all of which can be downloaded for free along with syntax files from the author’s website: http://mason.gmu.edu/~tlewis18/. While other books may touch on some of the same issues and nuances of complex survey data analysis, none features SAS exclusively and as exhaustively. Another unique aspect of this book is its abundance of handy workarounds for certain techniques not yet supported as of SAS Version 9.4, such as the ratio estimator for a total and the bootstrap for variance estimation. Taylor H. Lewis is a PhD graduate of the Joint Program in Survey Methodology at the University of Maryland, College Park, and an adjunct professor in the George Mason University Department of Statistics. An avid SAS user for 15 years, he is a SAS Certified Advanced programmer and a nationally recognized SAS educator who has produced dozens of papers and workshops illustrating how to efficiently and effectively conduct statistical analyses using SAS.
